# overfitting问题  -->  dropout

import tensorflow as tf

from sklearn.datasets import load_digits
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import LabelBinarizer


# load data

digits = load_digits()

X = digits.data
y = digits.target
y = LabelBinarizer().fit_transform(y)
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=.3)

def add_layer(inputs,in_size,out_size,activation_function=None):
    Weights = tf.Variable(tf.random_normal([in_size,out_size]))
    biases = tf.Variable(tf.zeros([1,out_size]) + 0.1)
    Wx_plus_b = tf.matmul(inputs,Weights) + biases

    if activation_function is None :
        outputs = Wx_plus_b
    else:
        outputs = activation_function(Wx_plus_b)

    return outputs


xs = tf.placeholder(tf.float32,[None,64])
ys = tf.placeholder(tf.float32,[None,10])



# lost

